更换文档检测模型

paddle_detection/ppdet/modeling/tests/test_ops.py

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+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+import os, sys
+# add python path of PaddleDetection to sys.path
+parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 4)))
+if parent_path not in sys.path:
+    sys.path.append(parent_path)
+
+import unittest
+import numpy as np
+
+import paddle
+
+import ppdet.modeling.ops as ops
+from ppdet.modeling.tests.test_base import LayerTest
+
+
+def make_rois(h, w, rois_num, output_size):
+    rois = np.zeros((0, 4)).astype('float32')
+    for roi_num in rois_num:
+        roi = np.zeros((roi_num, 4)).astype('float32')
+        roi[:, 0] = np.random.randint(0, h - output_size[0], size=roi_num)
+        roi[:, 1] = np.random.randint(0, w - output_size[1], size=roi_num)
+        roi[:, 2] = np.random.randint(roi[:, 0] + output_size[0], h)
+        roi[:, 3] = np.random.randint(roi[:, 1] + output_size[1], w)
+        rois = np.vstack((rois, roi))
+    return rois
+
+
+def softmax(x):
+    # clip to shiftx, otherwise, when calc loss with
+    # log(exp(shiftx)), may get log(0)=INF
+    shiftx = (x - np.max(x)).clip(-64.)
+    exps = np.exp(shiftx)
+    return exps / np.sum(exps)
+
+
+class TestROIAlign(LayerTest):
+    def test_roi_align(self):
+        b, c, h, w = 2, 12, 20, 20
+        inputs_np = np.random.rand(b, c, h, w).astype('float32')
+        rois_num = [4, 6]
+        output_size = (7, 7)
+        rois_np = make_rois(h, w, rois_num, output_size)
+        rois_num_np = np.array(rois_num).astype('int32')
+        with self.static_graph():
+            inputs = paddle.static.data(
+                name='inputs', shape=[b, c, h, w], dtype='float32')
+            rois = paddle.static.data(
+                name='rois', shape=[10, 4], dtype='float32')
+            rois_num = paddle.static.data(
+                name='rois_num', shape=[None], dtype='int32')
+
+            output = paddle.vision.ops.roi_align(
+                x=inputs,
+                boxes=rois,
+                boxes_num=rois_num,
+                output_size=output_size)
+            output_np, = self.get_static_graph_result(
+                feed={
+                    'inputs': inputs_np,
+                    'rois': rois_np,
+                    'rois_num': rois_num_np
+                },
+                fetch_list=output,
+                with_lod=False)
+
+        with self.dynamic_graph():
+            inputs_dy = paddle.to_tensor(inputs_np)
+            rois_dy = paddle.to_tensor(rois_np)
+            rois_num_dy = paddle.to_tensor(rois_num_np)
+
+            output_dy = paddle.vision.ops.roi_align(
+                x=inputs_dy,
+                boxes=rois_dy,
+                boxes_num=rois_num_dy,
+                output_size=output_size)
+            output_dy_np = output_dy.numpy()
+
+        self.assertTrue(np.array_equal(output_np, output_dy_np))
+
+    def test_roi_align_error(self):
+        with self.static_graph():
+            inputs = paddle.static.data(
+                name='inputs', shape=[2, 12, 20, 20], dtype='float32')
+            rois = paddle.static.data(
+                name='data_error', shape=[10, 4], dtype='int32', lod_level=1)
+            self.assertRaises(
+                TypeError,
+                paddle.vision.ops.roi_align,
+                input=inputs,
+                rois=rois,
+                output_size=(7, 7))
+
+        paddle.disable_static()
+
+
+class TestROIPool(LayerTest):
+    def test_roi_pool(self):
+        b, c, h, w = 2, 12, 20, 20
+        inputs_np = np.random.rand(b, c, h, w).astype('float32')
+        rois_num = [4, 6]
+        output_size = (7, 7)
+        rois_np = make_rois(h, w, rois_num, output_size)
+        rois_num_np = np.array(rois_num).astype('int32')
+        with self.static_graph():
+            inputs = paddle.static.data(
+                name='inputs', shape=[b, c, h, w], dtype='float32')
+            rois = paddle.static.data(
+                name='rois', shape=[10, 4], dtype='float32')
+            rois_num = paddle.static.data(
+                name='rois_num', shape=[None], dtype='int32')
+
+            output = paddle.vision.ops.roi_pool(
+                x=inputs,
+                boxes=rois,
+                boxes_num=rois_num,
+                output_size=output_size)
+            output_np, = self.get_static_graph_result(
+                feed={
+                    'inputs': inputs_np,
+                    'rois': rois_np,
+                    'rois_num': rois_num_np
+                },
+                fetch_list=[output],
+                with_lod=False)
+
+        with self.dynamic_graph():
+            inputs_dy = paddle.to_tensor(inputs_np)
+            rois_dy = paddle.to_tensor(rois_np)
+            rois_num_dy = paddle.to_tensor(rois_num_np)
+
+            output_dy = paddle.vision.ops.roi_pool(
+                x=inputs_dy,
+                boxes=rois_dy,
+                boxes_num=rois_num_dy,
+                output_size=output_size)
+            output_dy_np = output_dy.numpy()
+
+        self.assertTrue(np.array_equal(output_np, output_dy_np))
+
+    def test_roi_pool_error(self):
+        with self.static_graph():
+            inputs = paddle.static.data(
+                name='inputs', shape=[2, 12, 20, 20], dtype='float32')
+            rois = paddle.static.data(
+                name='data_error', shape=[10, 4], dtype='int32', lod_level=1)
+            self.assertRaises(
+                TypeError,
+                paddle.vision.ops.roi_pool,
+                input=inputs,
+                rois=rois,
+                output_size=(7, 7))
+
+        paddle.disable_static()
+
+
+class TestPriorBox(LayerTest):
+    def test_prior_box(self):
+        input_np = np.random.rand(2, 10, 32, 32).astype('float32')
+        image_np = np.random.rand(2, 10, 40, 40).astype('float32')
+        min_sizes = [2, 4]
+        with self.static_graph():
+            input = paddle.static.data(
+                name='input', shape=[2, 10, 32, 32], dtype='float32')
+            image = paddle.static.data(
+                name='image', shape=[2, 10, 40, 40], dtype='float32')
+
+            box, var = ops.prior_box(
+                input=input,
+                image=image,
+                min_sizes=min_sizes,
+                clip=True,
+                flip=True)
+            box_np, var_np = self.get_static_graph_result(
+                feed={
+                    'input': input_np,
+                    'image': image_np,
+                },
+                fetch_list=[box, var],
+                with_lod=False)
+
+        with self.dynamic_graph():
+            inputs_dy = paddle.to_tensor(input_np)
+            image_dy = paddle.to_tensor(image_np)
+
+            box_dy, var_dy = ops.prior_box(
+                input=inputs_dy,
+                image=image_dy,
+                min_sizes=min_sizes,
+                clip=True,
+                flip=True)
+            box_dy_np = box_dy.numpy()
+            var_dy_np = var_dy.numpy()
+
+        self.assertTrue(np.array_equal(box_np, box_dy_np))
+        self.assertTrue(np.array_equal(var_np, var_dy_np))
+
+    def test_prior_box_error(self):
+        with self.static_graph():
+            input = paddle.static.data(
+                name='input', shape=[2, 10, 32, 32], dtype='int32')
+            image = paddle.static.data(
+                name='image', shape=[2, 10, 40, 40], dtype='int32')
+            self.assertRaises(
+                TypeError,
+                ops.prior_box,
+                input=input,
+                image=image,
+                min_sizes=[2, 4],
+                clip=True,
+                flip=True)
+
+        paddle.disable_static()
+
+
+class TestMulticlassNms(LayerTest):
+    def test_multiclass_nms(self):
+        boxes_np = np.random.rand(10, 81, 4).astype('float32')
+        scores_np = np.random.rand(10, 81).astype('float32')
+        rois_num_np = np.array([2, 8]).astype('int32')
+        with self.static_graph():
+            boxes = paddle.static.data(
+                name='bboxes',
+                shape=[None, 81, 4],
+                dtype='float32',
+                lod_level=1)
+            scores = paddle.static.data(
+                name='scores', shape=[None, 81], dtype='float32', lod_level=1)
+            rois_num = paddle.static.data(
+                name='rois_num', shape=[None], dtype='int32')
+
+            output = ops.multiclass_nms(
+                bboxes=boxes,
+                scores=scores,
+                background_label=0,
+                score_threshold=0.5,
+                nms_top_k=400,
+                nms_threshold=0.3,
+                keep_top_k=200,
+                normalized=False,
+                return_index=True,
+                rois_num=rois_num)
+            out_np, index_np, nms_rois_num_np = self.get_static_graph_result(
+                feed={
+                    'bboxes': boxes_np,
+                    'scores': scores_np,
+                    'rois_num': rois_num_np
+                },
+                fetch_list=output,
+                with_lod=True)
+            out_np = np.array(out_np)
+            index_np = np.array(index_np)
+            nms_rois_num_np = np.array(nms_rois_num_np)
+
+        with self.dynamic_graph():
+            boxes_dy = paddle.to_tensor(boxes_np)
+            scores_dy = paddle.to_tensor(scores_np)
+            rois_num_dy = paddle.to_tensor(rois_num_np)
+
+            out_dy, index_dy, nms_rois_num_dy = ops.multiclass_nms(
+                bboxes=boxes_dy,
+                scores=scores_dy,
+                background_label=0,
+                score_threshold=0.5,
+                nms_top_k=400,
+                nms_threshold=0.3,
+                keep_top_k=200,
+                normalized=False,
+                return_index=True,
+                rois_num=rois_num_dy)
+            out_dy_np = out_dy.numpy()
+            index_dy_np = index_dy.numpy()
+            nms_rois_num_dy_np = nms_rois_num_dy.numpy()
+
+        self.assertTrue(np.array_equal(out_np, out_dy_np))
+        self.assertTrue(np.array_equal(index_np, index_dy_np))
+        self.assertTrue(np.array_equal(nms_rois_num_np, nms_rois_num_dy_np))
+
+    def test_multiclass_nms_error(self):
+        with self.static_graph():
+            boxes = paddle.static.data(
+                name='bboxes', shape=[81, 4], dtype='float32', lod_level=1)
+            scores = paddle.static.data(
+                name='scores', shape=[81], dtype='float32', lod_level=1)
+            rois_num = paddle.static.data(
+                name='rois_num', shape=[40, 41], dtype='int32')
+            self.assertRaises(
+                TypeError,
+                ops.multiclass_nms,
+                boxes=boxes,
+                scores=scores,
+                background_label=0,
+                score_threshold=0.5,
+                nms_top_k=400,
+                nms_threshold=0.3,
+                keep_top_k=200,
+                normalized=False,
+                return_index=True,
+                rois_num=rois_num)
+
+
+class TestMatrixNMS(LayerTest):
+    def test_matrix_nms(self):
+        N, M, C = 7, 1200, 21
+        BOX_SIZE = 4
+        nms_top_k = 400
+        keep_top_k = 200
+        score_threshold = 0.01
+        post_threshold = 0.
+
+        scores_np = np.random.random((N * M, C)).astype('float32')
+        scores_np = np.apply_along_axis(softmax, 1, scores_np)
+        scores_np = np.reshape(scores_np, (N, M, C))
+        scores_np = np.transpose(scores_np, (0, 2, 1))
+
+        boxes_np = np.random.random((N, M, BOX_SIZE)).astype('float32')
+        boxes_np[:, :, 0:2] = boxes_np[:, :, 0:2] * 0.5
+        boxes_np[:, :, 2:4] = boxes_np[:, :, 2:4] * 0.5 + 0.5
+
+        with self.static_graph():
+            boxes = paddle.static.data(
+                name='boxes', shape=[N, M, BOX_SIZE], dtype='float32')
+            scores = paddle.static.data(
+                name='scores', shape=[N, C, M], dtype='float32')
+            out, index, _ = ops.matrix_nms(
+                bboxes=boxes,
+                scores=scores,
+                score_threshold=score_threshold,
+                post_threshold=post_threshold,
+                nms_top_k=nms_top_k,
+                keep_top_k=keep_top_k,
+                return_index=True)
+            out_np, index_np = self.get_static_graph_result(
+                feed={'boxes': boxes_np,
+                      'scores': scores_np},
+                fetch_list=[out, index],
+                with_lod=True)
+
+        with self.dynamic_graph():
+            boxes_dy = paddle.to_tensor(boxes_np)
+            scores_dy = paddle.to_tensor(scores_np)
+
+            out_dy, index_dy, _ = ops.matrix_nms(
+                bboxes=boxes_dy,
+                scores=scores_dy,
+                score_threshold=score_threshold,
+                post_threshold=post_threshold,
+                nms_top_k=nms_top_k,
+                keep_top_k=keep_top_k,
+                return_index=True)
+            out_dy_np = out_dy.numpy()
+            index_dy_np = index_dy.numpy()
+
+        self.assertTrue(np.array_equal(out_np, out_dy_np))
+        self.assertTrue(np.array_equal(index_np, index_dy_np))
+
+    def test_matrix_nms_error(self):
+        with self.static_graph():
+            bboxes = paddle.static.data(
+                name='bboxes', shape=[7, 1200, 4], dtype='float32')
+            scores = paddle.static.data(
+                name='data_error', shape=[7, 21, 1200], dtype='int32')
+            self.assertRaises(
+                TypeError,
+                ops.matrix_nms,
+                bboxes=bboxes,
+                scores=scores,
+                score_threshold=0.01,
+                post_threshold=0.,
+                nms_top_k=400,
+                keep_top_k=200,
+                return_index=True)
+
+        paddle.disable_static()
+
+
+class TestBoxCoder(LayerTest):
+    def test_box_coder(self):
+
+        prior_box_np = np.random.random((81, 4)).astype('float32')
+        prior_box_var_np = np.random.random((81, 4)).astype('float32')
+        target_box_np = np.random.random((20, 81, 4)).astype('float32')
+
+        # static
+        with self.static_graph():
+            prior_box = paddle.static.data(
+                name='prior_box', shape=[81, 4], dtype='float32')
+            prior_box_var = paddle.static.data(
+                name='prior_box_var', shape=[81, 4], dtype='float32')
+            target_box = paddle.static.data(
+                name='target_box', shape=[20, 81, 4], dtype='float32')
+
+            boxes = ops.box_coder(
+                prior_box=prior_box,
+                prior_box_var=prior_box_var,
+                target_box=target_box,
+                code_type="decode_center_size",
+                box_normalized=False)
+
+            boxes_np, = self.get_static_graph_result(
+                feed={
+                    'prior_box': prior_box_np,
+                    'prior_box_var': prior_box_var_np,
+                    'target_box': target_box_np,
+                },
+                fetch_list=[boxes],
+                with_lod=False)
+
+        # dygraph
+        with self.dynamic_graph():
+            prior_box_dy = paddle.to_tensor(prior_box_np)
+            prior_box_var_dy = paddle.to_tensor(prior_box_var_np)
+            target_box_dy = paddle.to_tensor(target_box_np)
+
+            boxes_dy = ops.box_coder(
+                prior_box=prior_box_dy,
+                prior_box_var=prior_box_var_dy,
+                target_box=target_box_dy,
+                code_type="decode_center_size",
+                box_normalized=False)
+
+            boxes_dy_np = boxes_dy.numpy()
+
+            self.assertTrue(np.array_equal(boxes_np, boxes_dy_np))
+
+    def test_box_coder_error(self):
+        with self.static_graph():
+            prior_box = paddle.static.data(
+                name='prior_box', shape=[81, 4], dtype='int32')
+            prior_box_var = paddle.static.data(
+                name='prior_box_var', shape=[81, 4], dtype='float32')
+            target_box = paddle.static.data(
+                name='target_box', shape=[20, 81, 4], dtype='float32')
+
+            self.assertRaises(TypeError, ops.box_coder, prior_box,
+                              prior_box_var, target_box)
+
+        paddle.disable_static()
+
+
+if __name__ == '__main__':
+    unittest.main()